Welcome to the Manchester Summer School#
Spatial Analysis of Planning Applications and Sites of Biological Interest in Greater Manchester#
The aim of this project is to investigate the extent to which future property development threatens biodiversity in the Greater Manchester region. We are using spatial data supplied by the Greater Manchester Combined Authority and the Greater Manchester Ecology Unit to indentify if Sites of Biological Importance within the Greater Manchester region are at risk from future residential and/or commercial property development. We are using Jupyter and GitHub Pages to write, build and publish a website that incorporates discussion of the methodology and results with the code and its outputs.
Use the map below to investigate the spatial data we are using for this project:
Show code cell source
import geopandas as gpd
import pandas as pd
# Load and join GMCA housing, industrial and office supply data
housing_supply_gdf = gpd.read_file("data/gmca_data/2024 GM Housing Land Supply GIS.shp")
industrial_supply_gdf = gpd.read_file("data/gmca_data/2024 GM Industrial-warehousing Land Supply GIS.shp")
offices_supply_gdf = gpd.read_file("data/gmca_data/2024 GM Offices Land Supply GIS.shp")
total_supply_gdf = pd.concat(
[housing_supply_gdf, industrial_supply_gdf, offices_supply_gdf]
)
# Load and tidy GMEU Sites of Biological Importance data
sbi_gdf = gpd.read_file("data/gmeu_data/gm_sbi.shp")
sbi_gdf["Category"] = "Site of Biological Importance"
sbi_gdf = sbi_gdf.rename(columns = {"district": "LAName", "site_nam": "SiteRef"})
# Join GMCA and GMEU data
full_data_gdf = pd.concat(
[total_supply_gdf, sbi_gdf[["SiteRef", "LAName", "Category", "geometry"]]]
)
# Explore the data
full_data_gdf.explore("Category", cmap="tab10", tiles="CartoDB positron")
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